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Damage detection of structures identified with deterministic-stochastic models using seismic data.

Ming-Chih Huang1, Yen-Po Wang2, Ming-Lian Chang2

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Summary
This summary is machine-generated.

This study uses a deterministic-stochastic subspace identification method to detect structural damage in a five-storey steel frame. The damage locating vector (DLV) method successfully identified various damage scenarios using shaking table test data.

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Area of Science:

  • Structural Health Monitoring
  • System Identification
  • Seismic Engineering

Background:

  • Accurate structural health monitoring is crucial for assessing the safety and integrity of buildings, especially after seismic events.
  • Existing methods for damage detection often require detailed sensor networks or specific excitation types.
  • Developing robust techniques for identifying structural damage from global responses is an ongoing challenge in civil engineering.

Purpose of the Study:

  • To identify equivalent single-input-multiple-output system parameters of a discrete-time state equation using a deterministic-stochastic subspace identification method.
  • To apply the damage locating vector (DLV) method for detecting single and multiple damage conditions in a five-storey steel frame.
  • To evaluate the effectiveness, robustness, and limitations of the proposed damage detection scheme under various observation conditions.

Main Methods:

  • Deterministic-stochastic subspace identification for system parameter estimation.
  • Damage locating vector (DLV) analysis for damage detection.
  • Experimental validation using shaking table tests on a five-storey steel frame.

Main Results:

  • The deterministic-stochastic subspace identification method successfully identified system parameters under both full and partial observation conditions.
  • The damage locating vector (DLV) method effectively detected single and multiple damage locations in the steel frame.
  • Damaged stories were identifiable from global structural responses, particularly under sufficient observation.

Conclusions:

  • The proposed method, combining deterministic-stochastic subspace identification and DLV, offers a viable approach for structural damage detection.
  • The technique demonstrates robustness and effectiveness in localizing damage in frame systems, even for new or extended damages.
  • Sufficient observation of structural responses is key to accurately identifying damaged stories.